Download Download KnowledgeMiner 3.0 Here Free and Try It Out! 3/23/99 Downloads Complete KnowledgeMiner Download = Required Download + Examples Collection + Documentation Full Download (2.8 MB) Required + Examples Download (2.2 MB) + Documentation (650 KB) Required (1.1 MB) + Examples (1.1 MB) + Documentation (650 KB) System Requirements MacOS 7.0 + AppleGuide + QuickTime or MacOS 7.5 or newer any PowerPC based Apple Macintosh 32+ MB RAM recommended KnowledgeMiner Editions This table outlines the main features of the editions. For a more detailed view, see below. For prices, click here. Knowledge Miner GMDH (max. inputs) Analog Complexing Fuzzy Rule Induction Data Sheet rows/ cols. Copper x (50) - - 3,000/ 100 Silver x (500) x - 3,000/ 100 Gold x (500) x x 10,000/ 200 Demo* x (50) x x 3,000/ 100 * cannot save and print; GMDH and Fuzzy are limited to 4 layers; AC is limited to a pattern length of 10 and a data length of 500 KnowledgeMiner Copper spreasheet like handling of data including simple formulas and cell references several built-in mathematical functions for extending the data basis: xy, x(y/z), trigonometric, exponential and logarithmic functions, mean, sum and standard deviation, correlation analysis, random values, add uniform noise opens ASCII text files creates automatically linear or nonlinear static GMDH-models multi-input/single-output models as well as multi-input/multi-output models (system of equations) available analytically and graphically linear or nonlinear dynamic GMDH-models time series models, multi-input/single-output models as well as multi-input/multi-output models (predictable system of equations) available analytically and graphically for up to 50 input variables enables background modeling stores all created models in a model base dynamically all models can be used for status-quo or what-if predictions, classification or diagnosis problems within KnowledgeMiner KnowledgeMiner Silver Additional features to Copper: creates GMDH-models automatically for up to 500 input variables (for more variables, call) enabling solution of complex real-world problems creates nonparametric prediction models for fuzzy objects by Analog Complexing, an advanced pattern search technology for evolutionary processes. A synthesis of different prediction models (GMDH-based and Analog Complexing-based) is now possible as a powerful way to increase prediction accuracy. KnowledgeMiner Gold Additional features to Silver: provides Fuzzy Rule Induction as a third self-organizing data mining method for modeling, classification and prediction tasks extended data sheet for large data sets (up to 10,000 rows/ 200 columns) Unique Features GMDH-type Neural Networks that perform Active Neurons selecting their input variables themselves advanced network synthesis and model validation techniques to end up in a robust, optimal complex model creation of a best and autonomous system of equations (network of GMDH-type Neural Networks) which is ready for status-quo predictions of the complete system by default and which is available analytically and graphically (system graph) for results interpretation Analog Complexing as a powerful pattern search technology to create predictions for fuzzy processes (the most market processes e.g.) which other methods may be not appropriate for. Fuzzy Rule Induction from data to describe objects in a more natural language qualitatively explanatory power of any created model by default a model base to store all models and to keep connected information together completely autonomous modeling process that can work as background process on your Mac saving your resources either by working simultaneously with the modeling process or, for larger problems, by running the process overnight "I like KnowledgeMiner because its algorithm does not make any assumtions on the underlying data; well, at least not during the initial model-building phase. I also like the fact that it generates sets of equations that the user can review with detailed understanding of the interactions and dependencies of each variable. Also, the algorithm(s) behave surprising well under extreme conditions for certain complex dynamical systems. Congratulations for your excellent work. I was eagerly awaiting the PPC version. Thank you." Alexis, Pfizer Inc. "I have purchased your program KnowledgeMiner and have had some time to use it. My research is in artificial intelligence applications in clinical medicine at the University of Western Ontario in London, Canada. I have so far used backward error propagation and probalistic ANNs for outcomes based research. I also have some experience with fuzzy decision theory and expert systems. Your program looks interesting and has some advantages over my current modelling software (ie. NeuroSMARTs, Brainmaker and Neuralyst). ... I wish to congratulate you on your very promising software." Wayne, Associate Professor of Medicine, Division of Cardiology, University of Western Ontario, London, Canada "KnowledgeMiner is the only product that I have found that makes it easy to try non-standard equation formats on a data set. Many standard regression tools are as easy, but they limit you to a small set of potential relationships. KnowledgeMiner combines spreadsheet-like set up with an algorithm that doesn't "over fit" the model. Also, the output is in a readily usable format (e.g. not C++ code)." Ware, Dean & Company "Alpine skiing and athletic french federation have contacted my laboratory to build a profile of their elite athletes. In this case KnowledgeMiner helped me safe a lot of time and gave me models on the most important variables, and pointed out the less relevant." Fabrice, Laboratoire de Physiologie, Faculte de Medecine "I would just like to congratulate you on this program on the behalf of Roger Bradbury who did some work on GMDH back in 1988 (Green, D. G., Reichelt, R. E., and Bradbury, R. H. (1988) Statistical Behaviour of the GMDH algorithm. Biometrics 44: 49-69). He is wrapped that there is a modern version of it - of which we will definitely be purchasing." Belinda, Bureau of Resource Sciences, Australia "Herbert Fluhler, a senior radar engineer at Vista Technologies Inc. of Huntsville, Ala., has used the program to create vision-processing systems for disease diagnosis and target identification systems for the military. He called the program 'very powerful' and said its use of self-organizing algorithms gave him greater flexibility than more traditional methods such as neural networks or statistics." MacWeek, 11(1997)40 "I am Head of Computing and Information Technology at Katikati College, a high school in New Zealand. A couple of weeks ago I attended a big AppleFest at Rotorua with attendees from about 120 different schools. One key speaker delivered a workshop where he named the two big Mac products of 1998, one was Myrmidon and the other one (of course) was KnowledgeMiner. I have downloaded the demo and it appeals to me because of my interest in AI in general and neural nets in particular." John, Katikati College, New Zealand "I'm a physicist by training, working as a radar engineer on some cutting edge target recognition/classification technologies. I am now using KM to circumvent all the past pattern recognition algorithms which have been years (and millions of $) in development by the armed forces. Although I am just now starting to use KM in this application, my initial indications are that KM is providing a more robust,complete and more accurate classification capability than any of the previously used algorithms, and with comparatively no effort on my part" Herb, Vista Technologies, Inc.

Contact: julian@scriptsoftware.com      knowledgeminer@iworld.to   Date Last Modified: 03/23/99